sklearn.metrics.pairwise.euclidean_distances

sklearn.metrics.pairwise.euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False)

Considering the rows of X (and Y=X) as vectors, compute the distance matrix between each pair of vectors.

For efficiency reasons, the euclidean distance between a pair of row vector x and y is computed as:

dist(x, y) = sqrt(dot(x, x) - 2 * dot(x, y) + dot(y, y))

This formulation has two advantages over other ways of computing distances. First, it is computationally efficient when dealing with sparse data. Second, if x varies but y remains unchanged, then the right-most dot product dot(y, y) can be pre-computed.

However, this is not the most precise way of doing this computation, and the distance matrix returned by this function may not be exactly symmetric as required by, e.g., scipy.spatial.distance functions.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples_1, n_features)

Y : {array-like, sparse matrix}, shape (n_samples_2, n_features)

Y_norm_squared : array-like, shape (n_samples_2, ), optional

Pre-computed dot-products of vectors in Y (e.g., (Y**2).sum(axis=1))

squared : boolean, optional

Return squared Euclidean distances.

Returns:

distances : {array, sparse matrix}, shape (n_samples_1, n_samples_2)

See also

paired_distances

distances betweens pairs of elements of X and Y.

Examples

>>> from sklearn.metrics.pairwise import euclidean_distances
>>> X = [[0, 1], [1, 1]]
>>> # distance between rows of X
>>> euclidean_distances(X, X)
array([[ 0.,  1.],
       [ 1.,  0.]])
>>> # get distance to origin
>>> euclidean_distances(X, [[0, 0]])
array([[ 1.        ],
       [ 1.41421356]])

Examples using sklearn.metrics.pairwise.euclidean_distances

The Johnson-Lindenstrauss bound for embedding with random projections